D2D-Enabled Reliable Data Collection for Mobile Crowd Sensing

Abstract

With increasing more powerful sensing capacities of mobile devices, the Mobile Crowd Sensing (MCS) system requires to collect larger sensing data from participants. Nevertheless, collecting such large volume of data will cost a lot for participants, base stations and MCS server. Even worse, some sensing data cannot satisfy the MCS sensing requirement due to the low quality and are filtered by the MCS server in clouds. Inspired by the D2D technique, where mobile devices can communicate directly with the help of the nearby base station, in 5G networks, we propose the Reliable Data Collection (RDC) algorithm to validate the generated sensing data at device sides in this paper. To be specific, the whole progress is formulated as a Probability problem of Discovering Reliable sensing data (PDR) at client sides, and Expectation Maximization (EM) is leveraged to devise the algorithm. Finally, the extensive simulations and real-world use case are conducted to evaluate the performance of RDC algorithm, and the result shows that RDC outperforms the other two benchmarks in estimating accuracy and saving data collection cost.